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A hierarchal framework for recognising activities of daily life

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Abstract

In today’s working world the elderly who are dependent can sometimes be
neglected by society. Statistically, after toddlers it is the elderly who are observed
to have higher accident rates while performing everyday activities. Alzheimer’s
disease is one of the major impairments that elderly people suffer from, and leads
to the elderly person not being able to live an independent life due to forgetfulness.
One way to support elderly people who aspire to live an independent life and
remain safe in their home is to find out what activities the elderly person is
carrying out at a given time and provide appropriate assistance or institute
safeguards.
The aim of this research is to create improved methods to identify tasks related to
activities of daily life and determine a person’s current intentions and so reason
about that person’s future intentions. A novel hierarchal framework has been
developed, which recognises sensor events and maps them to significant activities
and intentions. As privacy is becoming a growing concern, the monitoring of an
individual’s behaviour can be seen as intrusive. Hence, the monitoring is based
around using simple non intrusive sensors and tags on everyday objects that are
used to perform daily activities around the home. Specifically there is no use of
any cameras or visual surveillance equipment, though the techniques developed
are still relevant in such a situation.
Models for task recognition and plan recognition have been developed and tested
on scenarios where the plans can be interwoven. Potential targets are people in the
first stages of Alzheimer’s disease and in the structuring of the library of kernel
plan sequences, typical routines used to sustain meaningful activity have been
used. Evaluations have been carried out using volunteers conducting activities of
daily life in an experimental home environment. The results generated from the
sensors have been interpreted and analysis of developed algorithms has been
made. The outcomes and findings of these experiments demonstrate that the
developed hierarchal framework is capable of carrying activity recognition as well
as being able to carry out intention analysis, e.g. predicting what activity they are
most likely to carry out next.